Increasing parameter accuracy of an agriculturally focused, spatially-explicit bee abundance model

Project Overview

Project Type: Graduate Student
Funds awarded in 2014: $14,652.00
Projected End Date: 12/31/2016
Grant Recipient: University of Maine
Region: Northeast
State: Maine
Graduate Student:
Faculty Advisor:
Frank A. Drummond
University of Maine, Dept of Biological Sciences
Faculty Advisor:
Dr. Cynthia Loftin
University of Maine

Annual Reports


  • Fruits: berries (blueberries), berries (other)
  • Animals: bees


  • Education and Training: decision support system, extension, focus group, workshop
  • Natural Resources/Environment: habitat enhancement, hedges - grass, hedges - woody, wetlands
  • Production Systems: agroecosystems
  • Sustainable Communities: partnerships, sustainability measures

    Proposal abstract:

    Wild lowbush blueberry (Vaccinium angustifolium Ait.) production currently relies heavily on honey bee pollination. Decrease in honey bee numbers and reduced bee quality may be contributing to pollination deficits, which has increased interest in reliance on native bee pollination of lowbush blueberries. Native bee abundance and diversity in lowbush blueberry fields is affected by the structure and composition of the surrounding landscape. Efforts to promote native bee habitat around lowbush blueberry fields in Maine may be more effective if native bee abundance in the landscape surrounding these fields could be predicted accurately. The InVEST Crop Pollination Model is a spatially-explicit model that uses land cover data to predict bee abundance in crop and non-crop cover types in agricultural landscapes. Application of this model requires bee habitat suitability scores that are typically informed by expert knowledge. Uncertainty that results from expert-derived scores potentially affects model prediction accuracy. My proposed research will use field surveys to quantify bee communities in non-crop land cover types in Maine’s lowbush blueberry growing regions. I hypothesize that field data-derived parameters will improve accuracy of model-predicted bee abundance compared to accuracy of expert opinion-derived parameters. This research will be the first comparison of expert opinion and bee community data from non-focal crops used to parameterize the InVEST Crop Pollination model. The information gained from this research will also be applied in a tool developed for lowbush blueberry growers to predict native bee abundance in their crop fields.

    Project objectives from proposal:

    Objective 1: Quantify bee community richness and abundance in non-blueberry cover types throughout Maine’s lowbush blueberry growing region. Data gathered for this objective will be used in analyses for Objective 2 to generate output used in Objective 3. Native bee abundance and diversity in Maine lowbush blueberry fields are negatively associated with proportion of coniferous forest and positively associated with proportion of deciduous forest at landscape scales (Chapin 2014); I expect field-collected data to reflect these predicted relationships. I also expect to find high bee abundance and diversity in wetlands and grasslands, which have blooming plants providing forage throughout the summer. I expect to find lower bee diversity and abundance in urban areas, which have less forage than natural areas.

    Objective 2: Compare predicted bee abundance from a spatially-explicit bee abundance model informed by expert opinion to abundance informed by bee community field data. Output from this objective will be used in Objective 3. I expect more accurate output from a model informed by field-collected data than a model informed by expert opinion, which is more subjective and can vary widely due to differing opinions.

    Objective 3: Develop a pollination tool for lowbush blueberry growers that presents information on native bee communities in and around crop fields, including an estimate of pollination service on their crop fields. Lowbush blueberry growers are aware of the importance of native bees to their crop and want more information regarding bee communities and their effects on crop yield (Hanes et al. 2013). Presenting an accurate model is critical to engaging growers’ interest in using this tool.

    Chapin, S. 2014. The application of spatial modeling tools to predict native bee abundance in Maine’s lowbush blueberries. Thesis, University of Maine.

    Hanes, S. P., et al. 2013. Grower perceptions of native pollinators and pollination strategies in the lowbush blueberry industry. Renewable Agriculture and Food Systems, 1–8.

    Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the view of the U.S. Department of Agriculture or SARE.